Monte Carlo Machine Learning Library

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The Monte Carlo Machine Learning Library (MCMLL) is an open source C++ template library which already relies on some C++0x specs. MCMLL is licensed under the GNU GPL. It is developed under the 64 bit Linux OS. MCMLL should be usable on other platforms as well, since it is based on ISO C++.
The philosophy behind MCMLL is to have a broad range support for Monte Carlo methods to implement machine learning applications. Since Monte Carlo methods are inherently parallelizable, the goal is to provide multi-threaded implementations of the most important methods.


MCMLL has an emphasis on Monte Carlo methods. Some main components of MCMLL are:

Example applications include:

Supported Evolutionary Algorithms[edit]

The supported EA's are:

Supported Sequential Monte Carlo Methods[edit]

For particle filtering, the Sequential Importance Resampling (SIR) method is supported. To create an SMC application based on MCMLL, one has to define an observation distribution, a transition distribution and optionally an importance distribution to be used in the SIR operator.

See also[edit]

  • MOEA Framework, an open source Java framework for multiobjective evolutionary algorithms
  • ECJ, a toolkit to implement evolutionary algorithms
  • Paradiseo, a metaheuristics framework


  1. ^ J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer. Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation, 2006. DOI 10.1109/TEVC.2006.872133
  2. ^ Jingqiao Zhang and Arthur C. Sanderson. 2009. JADE: adaptive differential evolution with optional external archive. Trans. Evol. Comp 13, 5 (October 2009), 945-958. DOI=10.1109/TEVC.2009.2014613
  3. ^ Onay Urfalioglu and Orhan Arikan, Randomized and Rank Based Differential Evolution, Machine Learning and Applications, Fourth International Conference on, vol. 0, isbn 978-0-7695-3926-3, 2009, pp 95--100,
  4. ^ Urfalioglu, Onay and Arikan, Orhan, Self-adaptive randomized and rank-based differential evolution for multimodal problems, Journal of Global Optimization, Springer Netherlands, 2011, pp 1--34,

External links[edit]